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In this video tutorial, the presenter addresses how to evaluate bounding box predictions in object detection. The focus is on measuring the accuracy of predicted bounding boxes compared to target bounding boxes using a metric called Intersection over Union (IoU). The tutorial will involve an explanation of this metric and a hands-on implementation in PyTorch. The presenter emphasizes the importance of quantifying the quality of predictions to effectively assess object detection performance. The session begins with an introduction to IoU and progresses to practical coding examples.